README.md

---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: meedan/paraphrase-filipino-mpnet-base-v2
datasets:
- bsen26/eyeR-classification-multi-label-category2
metrics:
- accuracy
widget:
- text: i ordered shake shake fries but they give me just the plain one!! there's
    no ketchup or any cutlery!!! i will only give you one star!! tsk poor service
    ??
- text: The fries were soggy and did not taste good, there was no cutlery, the butter
    was already melted when I got the order.
- text: i ordered crispy fillet ala king why no sauce ? and asked for iced tea and
    you give pineapple juice ? are you kidding me ? are you even reading some instructions?
- text: Wrong coffee / no ketchup / cold fries. Ugh
- text: They have forgot to put inside the toy i ordered, my child is dispointed because
    she's expecting the pikachu toy please fix this !!
pipeline_tag: text-classification
inference: false
model-index:
- name: SetFit with meedan/paraphrase-filipino-mpnet-base-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: bsen26/eyeR-classification-multi-label-category2
      type: bsen26/eyeR-classification-multi-label-category2
      split: test
    metrics:
    - type: accuracy
      value: 0.5407407407407407
      name: Accuracy
---

# SetFit with meedan/paraphrase-filipino-mpnet-base-v2

This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [bsen26/eyeR-classification-multi-label-category2](https://huggingface.co/datasets/bsen26/eyeR-classification-multi-label-category2) dataset that can be used for Text Classification. This SetFit model uses [meedan/paraphrase-filipino-mpnet-base-v2](https://huggingface.co/meedan/paraphrase-filipino-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Model Details

### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [meedan/paraphrase-filipino-mpnet-base-v2](https://huggingface.co/meedan/paraphrase-filipino-mpnet-base-v2)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 128 tokens
<!-- - **Number of Classes:** Unknown -->
- **Training Dataset:** [bsen26/eyeR-classification-multi-label-category2](https://huggingface.co/datasets/bsen26/eyeR-classification-multi-label-category2)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.5407   |

## Uses

### Direct Use for Inference

First install the SetFit library:

```bash
pip install setfit
```

Then you can load this model and run inference.

```python
from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("bsen26/eyeR-category2-multilabel")
# Run inference
preds = model("Wrong coffee / no ketchup / cold fries. Ugh")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 18.3958 | 41  |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0010 | 1    | 0.0919        | -               |
| 0.0521 | 50   | 0.1443        | -               |
| 0.1042 | 100  | 0.0682        | -               |
| 0.1562 | 150  | 0.1043        | -               |
| 0.2083 | 200  | 0.0653        | -               |
| 0.2604 | 250  | 0.0136        | -               |
| 0.3125 | 300  | 0.0025        | -               |
| 0.3646 | 350  | 0.0195        | -               |
| 0.4167 | 400  | 0.0073        | -               |
| 0.4688 | 450  | 0.0115        | -               |
| 0.5208 | 500  | 0.0045        | -               |
| 0.5729 | 550  | 0.0052        | -               |
| 0.625  | 600  | 0.0091        | -               |
| 0.6771 | 650  | 0.0037        | -               |
| 0.7292 | 700  | 0.0027        | -               |
| 0.7812 | 750  | 0.0058        | -               |
| 0.8333 | 800  | 0.0118        | -               |
| 0.8854 | 850  | 0.0025        | -               |
| 0.9375 | 900  | 0.0005        | -               |
| 0.9896 | 950  | 0.0085        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.1
- Tokenizers: 0.19.1

## Citation

### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}
```

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